Lightweight Maize Ear Quality Identification Based on Spatial Information Enhancement
Cob,is the aggregated form of the seeds.In order to realize the accurate and fast recognition of corn cob quality by lightweight convolutional neural network,a corn cob quality recognition model LCPA-Ghost combi-ning lightweight backbone and lightweight channel pooling attention was proposed.Firstly,the Ghost network was used to achieve lightweight processing,reduce training cost and redundant information,and improve the feature learning ability of the model.Secondly,the LCPA module was added to the shortcut connection of Ghost module to make up for the lack of spatial information capture capabilities and ensure the model recognition accuracy by introdu-cing a few parameters.The experiments were conducted with normal,seed disorder,mildew,miscellaneous color and missing grain ears,and a base dataset containing 1 571 images of cobs was collected and produced.The experimental results indicated that the test recognition rate of LCPA-Ghost model reached 98.12％,comparable to CorNet,while the number of model parameters was only 2.40 M,and the single recognition speed was 19.08 ms,with an improve-ment of 9.8％.The LCPA-Ghost model provided a feasible experimental method for the lightweight identification of maize ear quality.
NETL
NSTL
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